Background: The debate on using concrete versus abstract materials in learning mathematics has been longstanding. For decades, research has focused on the physical characteristics of materials when defining them as concrete or abstract. Aims: This study extends the field by proposing a two-dimensional classification, which defines materials as…

This study aims to conduct a comparative study of Bagging and Boosting algorithms among ensemble methods and to compare the classification performance of TreeNet and Random Forest methods using these algorithms on the data extracted from ABIDE application in education. The main factor in choosing them for analyses is that they are Ensemble methods…

Correct specifications of hierarchical attribute structures in analyses using diagnostic classification models (DCMs) are pivotal because misspecifications can lead to biased parameter estimations and inaccurate classification profiles. This research is aimed to demonstrate DCM analyses with various hierarchical attribute structures via Bayesian…

This paper addresses the influence of ethnic and gender disparities on educational outcomes and recognizes the significance of maternal education in shaping students' academic achievements. The study aims to evaluate the efficacy of policies aimed at integrating maternal involvement in education among 15-year-old students from OECD countries.…

Children's math performance is strongly correlated with later life outcomes, but early gaps in math skills are stubbornly difficult to close. It is therefore important to identify student math needs early. Using Grade 1-3 student records from Kentucky public schools, the study finds that typically recommended cut scores for widely used early grade…

Learning Analytics is concerned with the use of data collected in educational settings to support learning processes. We take a Learning Analytics approach to study the use of immediate feedback in digital classification tasks in mathematics. Feedback serves as an opportunity for learning, however its mere existence does not guarantee its use and…

Considering the large volume of PISA data, it is expected that data mining will often be assisted in making PISA data more meaningful. Studies show that different dimensions of ICT may reveal different relationships for mathematics achievement. The purpose of this article is to evaluate the success of the decision tree classification algorithms in…

This paper introduces an approach that uses latent class analysis to identify cut scores (LCA-CS) and categorize respondents based on context scales derived from largescale assessments like PIRLS, TIMSS, and NAEP. Context scales use Likert scale items to measure latent constructs of interest and classify respondents into meaningful ordered…

In 2006-2007, California revised its English language proficiency (ELP) assessment, the California English Language Development Test (CELDT), which resulted in more stringent criteria and higher cut scores for meeting proficiency. Using regression discontinuity designs and a difference-in-differences approach, this study examines the effects of…

The purpose of this study was to compare the classification accuracy of data produced by the previous year's end-of-year New York state assessment, a computer-adaptive diagnostic assessment ("i-Ready"), and the gating combination of both assessments to predict the rate of students passing the following year's end-of-year state assessment…

This article presents a latent class model for multilevel data to identify latent subgroups and estimate heterogeneous treatment effects. Unlike sequential approaches that partition data first and then estimate average treatment effects (ATEs) within classes, we employ a Bayesian procedure to jointly estimate mixing probability, selection, and…

The aim of this paper is to review some studies conducted with different learning areas in which the schemes of different participants emerge. Also it is about to show how mathematical proofs are handled in these studies by considering Harel and Sowder's classification of proof schemes with specific examples. As a result, it was seen that the…

The current investigation used latent profile analysis (LPA) to categorise students according to their strategies for managing mathematics homework. A sample of 918 Chinese students in grades 7-9 rated their use of seven homework management strategies; namely, arranging the environment, managing time, monitoring motivation, managing emotion,…

In this paper, we present the development of an investigation on the promotion of covariational reasoning in high school students (14-15 years old) in Mexico. The study consists of designing and applying a sequence of didactic activities that simulate a real situation virtually. The activities are organized through a Hypothetical Learning…

Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB)…